Gesture Control Activation Regions to Reduce False Triggers
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Solution Overview
Problem
Conventional device control systems experience a high false drop rate due to unintentional gesture recognition, leading to inaccurate control of terminal devices.
Innovation Solution
A method and system that recognize environmental images to determine specific body parts for control, monitor target body part movements, and perform corresponding functions only when consistent with preset activation and control movements, reducing false triggers by using a two-stage movement verification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the terminal device continuously collects multiple frames of user images and identifies hand gestures to control the device, then the device can perform function operations based on hand movements, but the false drop rate increases due to unintentional gesture recognition
Solution Approach 1:
The gesture recognition process is divided into two independent stages: activation movement recognition and control movement recognition. The to-be-recognized region is further segmented into target to-be-recognized regions based on the activation movement. This segmentation ensures that both activation and control movements must be intentionally performed, significantly reducing false triggers from unintentional gestures while maintaining ease of operation.
2Measurement precision
If the system monitors target body part movements in the to-be-recognized region, then the accuracy of control movement recognition improves, but the system complexity increases due to the two-stage verification process
Solution Approach 1:
The system performs preliminary recognition of activation movements before proceeding to control movement recognition. When an activation movement is detected in the to-be-recognized region, the system dynamically determines target to-be-recognized regions and continues monitoring for control movements. This preliminary action filters out non-intentional gestures early, improving overall recognition accuracy while managing system complexity through conditional processing.
Data Source
AI summary
One example device control method includes: recognizing a collected environmental image; determining at least one to-be-recognized region in the environmental image; monitoring a target body part movement in the to-be-recognized region; if it is monitored that the target body part movement is consistent with a preset activation movement, tracking and monitoring a target body part movement in a target to-be-recognized region; or if it is monitored that the target body part movement in the target to-be-recognized region is a preset control movement, controlling a terminal device to perform a function operation corresponding to the control movement.